My research concerns philosophical issues in scientific modeling and experimentation, and emphasizes the transformative effect that new technologies have on these practices. General questions of interest include: What makes theoretical abstractions informative with respect to natural phenomena? How do we judge the reliability of our means of generating and processing data? How are such strategies affected by technological change, especially when these become automated and result in massive amounts of data? And how do these effects condition scientists’ conception of their target of inquiry, including socially salient categories like race and gender?

To address these, I focus on the related fields of molecular, systems, and computational­ biology, which have recently experienced a rise of new modeling and data-gathering practices pertinent to these issues. I am developing a framework that can address them from an integrated philosophical and historical standpoint. It proceeds along three strands: (1) understanding how models are inferentially connected to data-gathering practices in science; (2) clarifying the epistemic norms and frameworks guiding scientific methodology and explanation; (3) investigating the historical interplay of experimental technologies and the models of conceptions of natural phenomena.


“Observations, Experiments, and Arguments for Epistemic Superiority in Scientific Methodology” (with Nora Boyd), Philosophy of Science, 91(1): 111-131 (preprint)

“Empirical Techniques and the Accuracy of Scientific Representations,” Studies in the History and Philosophy of Science, Part A, 94: 143-157 (preprint)

“Mechanistic Explanation in Systems Biology: Cellular Networks,” British Journal for the Philosophy of Science, 68(1): 1-25 (pdf)

“Causal Concepts Guiding Model Specification in Systems Biology,” Disputatio, Special Issue on Causality and Modeling in the Sciences, 9(47): 499-527 (pdf)

“The Rise of Cryptographic Metaphors in Boyle and Their Use for the Mechanical Philosophy,” Studies in the History and Philosophy of Science, Part A, 73: 8-21 (pdf)

Sample works in progress

“Fine-Graining Accounts of Exploratory Experimentation”

Philosophers of science commonly approach both domains in terms of the distinction between (a) hypothesis- or theory-driven experimentation and (b) exploratory experimentation, typically locating these experiments within the latter category. This has led to debates over how theories relate to exploratory experimentation in these contexts. Such debates speak to a lack of specificity in the underlying concept of exploratory experimentation and illustrate the need for a more detailed understanding of the diverse ways that theory is related to experimental practice. Following this lead, I compare the data production and processing pipelines across recent experiments at ATLAS and in high-throughput biology, analyzing the factors that inform choices in each process. Going beyond the distinction between general and local theory, I argue for a “reticulated” account of the relation between theory and experimental practice. The differences between the two cases brought out by this account aid in diagnosing concerns over the reliability of recent methods in molecular biology.

“Understanding Through Coordinated Practices: Uses of Deep Learning in Structural Biology”

This paper connects structural biologists’ uses of new algorithms for protein structure prediction with philosophical discussion of the epistemology of machine learning. One prominent area of discussion concerns the epistemic opacity, interpretability, and explainability of deep learning models. I argue that structural biologists have developed methods and norms for using AI that, when properly followed, effectively circumvent the most serious obstacles to understanding. I highlight three interrelated reasons for this being the case and use these points to think about the relation between scientific understanding and machine learning algorithms. I propose we view scientific understanding as the outcome of a distributed but coordinated set of scientific activities, rather than something that resides in the internal content of a model.

“Pragmatic Representational Content”

I propose a notion of the representational content of scientific models that resists the dialectic over scientific representation inherited from philosophical semantics. This view neither reduces the content of models to cognitive functions, nor appeals to a model-target relation as that which determines a model’s content. Instead, I describe scientific representation as the use of a model within a system of practice. I argue that there are features of this practice, beyond a user’s cognitive acts, that determine the representational content of a model. Ultimately, I articulate a view of pragmatic representational content understood as the set of action- and expectation-guiding inferences contributed by a model when integrated with an empirical practice. Each of the key terms is given further explication, followed by an account of what “grasp” of this content consists in.

My research explores philosophical issues in scientific modeling and experimentation: What is the empirical significance of mathematical abstractions? How do they represent a target of inquiry? How do we judge the reliability of our means of generating and processing data, especially when these are automated and result in massive amounts of information?

To answer these questions, I focus on the fields of systems biology and biophysics, which have recently seen a rise in new modeling and data-gathering practices pertinent to these questions. I am developing a framework that can address these issues from an integrated philosophical and historical standpoint. It proceeds along three strands: (1) understanding how models are inferentially connected to data-gathering practices in science; (2) clarifying the role of mechanistic thinking in the biology of complex systems and its relation to mathematical explanation; (3) investigating the historical roots of distinct forms of knowledge revolving around experimentation.


In progress

with Nora Boyd (Siena): “Observations, Experiments, and Arguments for Epistemic Superiority in Scientific Methodology”

A long-standing tradition in philosophy of science accords epistemic superiority to experiment over observation in virtue of the benefits concomitant with physical manipulation of experimental systems. Physical manipulation especially is thought to confer fine-grained control and yield causal knowledge. This paper adds to extant arguments against general claims for the epistemic superiority of experiment over observation, considered as empirical data-gathering scientific practices. In particular, we argue that physical manipulation is neither necessary nor sufficient for achieving fine-grained control and generating causal knowledge. In addition, we articulate and defend some general features of empirical data-gathering scientific practices that we argue do generally confer epistemic superiority, but which also cross-cut the traditional distinction between observation and experiment.

“Portability and reliability: A constraint on the relational account of data”

Recent arguments for relational accounts of data criticize prior notions (such as that of Bogen and Woodward 1988) that emphasize the local and idiosyncratic relation between data and causal features of the experimental context that produced them. These earlier views, it is said, cannot account for the portability of data, i.e., the fact that researchers routinely use data generated in one context to reason about different phenomena in very different settings. In this paper I consider three ways that data are used to reason about phenomena in this way and argue that portability depends on the data being reliable with respect to their original source phenomenon. I show how this vindicates aspects of earlier views, but also prompts us to reconsider the relation between data and phenomenon that they propose.

“Reflections on history, technology, and the evaluation of methods”

Recent work on the history and philosophy of molecular biology has claimed that “top-down” model-building methods were superior to “bottom-up” data-driven methods for solving a major problem in the field: determining the structure of DNA. I aim to complicate that claim and caution against hasty generalization from this case in light of the current dominance of data-driven methods in the field and their successes in ensuing years. I argue that a broader historical perspective reveals that superiority judgments are dependent on the state of development of the relevant methods, understood in terms of the technological and epistemic resources available to researchers. This has metaphilosophical implications: namely, that philosophers seeking evidence of normative epistemological criteria in the history of science ought to consider the longer-term developmental history of a field alongside case studies of particular findings.

“The conceptual gains and empirical constraints on blood crystal research in nineteenth century physiological chemistry”

This paper traces the interplay between developments in techniques for preparing and studying blood crystals and the scientific conception of the contents of these crystals. This history is meant to illustrate a philosophical point concerning the ways scientific reasoning about a natural target is fundamentally constrained by the techniques through which it is characterized. As scientific questions came to outpace the resolving power of available techniques for investigating crystal contents, modeling of these contents hit a standstill that was only surmounted by development of novel techniques.